Nevertheless, a large amount of data for online learning may include some serious personal information, e.g., salary or medical records. Due to the distributed network topology, the information is transmitted and processed through mutual communication between neighboring nodes, and the information may be eavesdropped during the transmission, which may lead to the leakage of sensitive information. To address the privacy concern, this paper mainly focuses on the differential privacy mechanism, which scrambles the public information by adding a certain amount of noise, thus making it impossible for an attacker to learn the users' private data [1]}. Differential privacy has become widely popular among researchers, and it has developed extremely rich mathematical formulation and provable privacy properties. The basic idea of differential privacy-based algorithms is to inject random noise or bias in the nodes' communication or computation.
